Suppressing random noise is an important pre-processing step in the analysis of many signals. One area in which this pre-processing is important is in the analysis of seismic signals, where the suppression of random noise is advantageously implemented prior to applying an information-extraction algorithm such as a seismic edge detection or coherence cube algorithm. This pre-processing is valuable because the seismic data generally includes reflection data from around faults-and fractures in the ground, and this reflection data is usually more complicated and weaker than the data from other areas due to dispersion, diffraction and other forms of scattering.
Typically, prediction error filtering (PEF or f-x deconvolution) is used to precondition the data before edge detection. Prediction error filtering has been very successful in many areas. However, if the signal being pre-processed is not highly predictable, such as in areas of fault or fracture, this method is inadequate to remove the noise.
A simple alternative method is to smooth the data within moving windows. Unlike the PEF method, this smoothing method does not strongly depend on the predictability of the signals. The drawback here is that this method tends to blur the sharp edges that are associated with the faults and channels that are intended to be enhanced in seismic edge detection.